The early works in deep image clustering usually combine auto-encoders (AE) or Convolutional Neural Network (CNN) based representation learning with traditional shallow clustering methods [1]}, [2]}, [3]}, [4]}, [5]}. In recent years, with the rapid development of pre-training model, such as VGG-16 [6]}, Resnet [7]}, ViT [8]}, Swin Transformer [9]}, image clustering methods leave the images representation task for pre-training model, and directly map image representations into labels by training a classification model like Multilayer Perceptron (MLP), by maximizing the mutual information between the image and its augmentations [10]}, [11]}, [12]} or the likelihood of the cluster assignments between the image and its neighbors [13]}, [14]}, [12]}, [16]}. However, since we want to obtain semantically meaningful clusters, it is difficult to solve this problem by only exploring images. Figure REF  shows some examples which are semantically different but visually similar. For example, an image with an airplane may be visually similar to an image with a bird, and an image with a deer may be visually similar to an image with a horse.
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